An epoch is a training hyperparameter specifying one complete cycle through the full training dataset. Its parameter is the integer count of cycles; its persistence mechanism is procedural discipline — it structures the temporal organization of training by governing when the data iterator resets and the model re-exposes every sample. Each epoch increments the training clock, enabling convergence monitoring and early-stopping decisions. [formal: epocha | substrate: behavior | horizon: a life | explicit: yes | epoch: 0.01]
Accepted ontology entry
epochs
An epoch is a training hyperparameter specifying one complete cycle through the full training dataset. Its parameter is the integer count of cycles; its persistence mechanism is procedural discipline — it structures the temporal organizati…
Definition
Why it is in scope
One complete pass of a machine learning model through the entire training dataset during training. It is a training hyperparameter that controls how many full dataset cycles the optimization procedure performs.
Names and aliases
- epochsen · CANONICAL
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One epoch is defined as one complete pass through the training loop. Remove the training loop and the concept of epochs ceases to operate — epochs has no referent without the iterative training structure it governs.
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An epoch consists of multiple iterations (mini-batch steps). One epoch = one full pass through the dataset, divided into iterations. Whole→part per Law 10.
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Epochs existed first as a general optimization concept (iterations through data in iterative optimization algorithms) and fed into machine learning training procedures. Which-came-first test: the concept of iterating through data in optimization predates ML-specific use of 'epochs' — the term describes the same iterative loop used in numerical optimization before it was adopted in training pipelines.
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Record identity
- Created
- Aug 9, 2026, 12:17 PM UTC
- Content hash
- 50c7caea6c77b171a90d90924fd168082f2c9a0755be9da73f848d0159d75926